Evolutionary Learning Algorithms for Neural Adaptive Control is an advanced textbook, which investigates how neural networks and genetic algorithms can be applied to difficult adaptive control problems which conventional results are either unable to solve , or for which they can not provide satisfactory results. It focuses on the principles involved, rather than on the modelling of the applications themselves, and therefore provides the reader with a good introduction to the fundamental issues involved.

Unique selling points: - The book has been carefully written so that no prior knowledge of neural networks and genetic algorithms is needed - The author illustrates the basic principles of evolutionary learning algorithms by applying them to adaptive control problems - The book includes a chapter devoted to artificial neural networks, which is one of the most active areas of research at the moment

Inhalt
Introduction.- Dynamic systems and control.- The attitude control problem.- Artificial neural networks.- Neuromodels of dynamic systems.- Current neurocontrol techniques.- Genetic algorithms.- Adaptive control architectures.- Conclusions and the future.- A. Euler equations solutions.- B. An attitude control simulator.- Bibliography.- Index.